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How to use Google Cloud Datastore (Firestore in Datastore mode)?

Google Cloud Datastore, now often referred to as Firestore in Datastore mode, is a highly scalable, fully managed NoSQL document database provided by Google Cloud Platform (GCP). It's an excellent choice for applications that require strong consistency and a robust database solution without the operational overhead of managing traditional databases. This guide will walk you through everything you need to know to start using Datastore effectively.

1. Understanding Firestore in Datastore Mode

Firestore has two operational modes: Native mode and Datastore mode. While both offer similar functionality, there are key differences. Datastore mode is primarily designed for compatibility with existing App Engine applications and prioritizes consistency over immediate global updates (as opposed to Native mode). It offers:

• Strong Consistency: Ensures data is accurate and reliable across all reads and writes.
• Scalability: Handles massive amounts of data and traffic with automatic scaling.
• Fully Managed: No need to provision servers, install software, or handle backups.
• Tight Integration with App Engine: Designed for seamless integration with Google App Engine.
• GAE Features Support: Supports features like ancestor queries.
• Pricing Similarities: Cost model focused on reads, writes, storage.

Think of it this way: If you're building a new application from scratch *without* strong App Engine dependency, Firestore in Native mode might be preferable due to its more flexible querying and optimized write speeds. However, for App Engine projects or applications where strong consistency is paramount, Datastore mode is often the better choice.

2. Setting Up Your Google Cloud Project and Datastore

Before you can start using Datastore, you need a Google Cloud project. Here's how to set everything up:

1. Create a Google Cloud Project: If you don't already have one, create a new project in the Google Cloud Console.
2. Enable the Cloud Datastore API: Go to the API Library within your project (search for "Cloud Datastore API") and enable it.
3. Choose a Datastore Location: When you first access the Datastore dashboard (typically found by searching for "Datastore" in the Cloud Console), you will be prompted to select a location for your Datastore. Choose a location geographically close to your users for optimal performance.
4. Select Datastore Mode: Important: When prompted for the database mode during the setup, choose "Datastore mode". This is crucial, as the Native mode has different querying semantics and limitations.
5. Configure IAM Permissions: Grant appropriate IAM (Identity and Access Management) permissions to your application's service account or user accounts to allow them to access Datastore. Common roles include roles/datastore.user, roles/datastore.owner, and roles/datastore.viewer.

3. Data Modeling in Datastore

Datastore is a schemaless NoSQL database, but effective data modeling is crucial for performance and scalability. Here are the key concepts:

• Entities: An entity is a single object in Datastore. It represents a single, cohesive data unit (like a user profile, a product, or a blog post).
• Kind: A kind is a category or type of entity. It's similar to a table in a relational database. All entities of the same type belong to the same kind (e.g., User, Product, BlogPost).
• Properties: Each entity has properties, which are name-value pairs. Properties can store various data types, including strings, integers, floats, booleans, dates, and even embedded entities (useful for hierarchical data). Datastore's schemaless nature allows entities within the same kind to have different properties or property types.
• Key: Every entity has a unique key, which identifies it within the Datastore. The key consists of:
  
  • Kind: The kind of the entity.
  • ID or Name: Either a system-generated numerical ID or a developer-assigned string name. Choosing IDs is generally preferable as Datastore can assign IDs faster than assigning string Names. IDs are usually integers and autogenerated upon inserting the object to the Datastore. Names need to be specified during object insertion, can take longer to insert, but allow easier management since names can be understood in a meaningful manner.
  • Ancestor Path (Optional): An ordered list of ancestor keys. This creates a hierarchical structure within your data. Entities with the same ancestor are considered to be in the same entity group and support ACID transactions. Ancestor paths define the relationships between entities, making strongly consistent operations on related entities possible. This also means you should not use one long ancestor path to describe all of the elements inside Datastore as performance bottlenecks may happen.
  
  

Example: Modeling a User

Let's say you're building a simple application that needs to store user profiles. A good way to model this in Datastore would be:

• Kind: User
• Properties:
  
  • firstName: String
  • lastName: String
  • email: String
  • age: Integer
  • registrationDate: DateTime
  
  
• Key: The system will generate a unique ID for each user.

Example: Modeling a Blog Post with Comments

To demonstrate ancestor paths, let's model blog posts and their comments:

• Kind: BlogPost
• Properties:
  
  • title: String
  • content: String
  • author: String
  • publishDate: DateTime
  
  
• Key: The system will generate a unique ID for each blog post.
• Kind: Comment
• Properties:
  
  • author: String
  • text: String
  • commentDate: DateTime
  
  
• Key: The system will generate a unique ID for each comment, *with the BlogPost key as its ancestor.* This means that each comment is hierarchically associated with its parent blog post.

The ancestor path for a Comment on a specific BlogPost would look something like: Key('BlogPost', 12345), Key('Comment', 67890). This creates a hierarchical structure.

4. Interacting with Datastore: Code Examples

Google provides client libraries for interacting with Datastore in various programming languages, including Python, Java, Go, Node.js, PHP, and Ruby. We will provide python code snippet below, others are all quite similar.

Python Example: Creating and Saving an Entity

 from google.cloud import datastore

 # Instantiate a client
 client = datastore.Client()

 # Define the kind for the entity
 kind = 'Task'

 # The name/ID for the new entity
 name = 'sampletask'

 # The Cloud Datastore key for the new entity
 task_key = client.key(kind, name)

 # Prepares the new entity
 task = datastore.Entity(key=task_key)

 task['description'] = 'Buy milk'

 # Saves the entity
 client.put(task)

 print(f"Saved {task.key.name}: {task['description']}")
 

Python Example: Retrieving an Entity

 from google.cloud import datastore

 client = datastore.Client()

 kind = 'Task'
 name = 'sampletask'

 task_key = client.key(kind, name)

 # fetches the entity
 task = client.get(task_key)

 if task:
 print(f"Task: {task['description']}")
 else:
 print("Task not found.")
 

Python Example: Querying for Entities

 from google.cloud import datastore

 client = datastore.Client()

 kind = 'Task'

 query = client.query(kind=kind)
 query.add_filter('description', '=', 'Buy milk')

 results = list(query.fetch())

 for task in results:
 print(f"Task: {task.key.name}, Description: {task['description']}")
 

5. Queries in Datastore

Querying is a fundamental aspect of interacting with Datastore. Here are key considerations:

• Querying by Property: You can filter and sort entities based on their properties using various comparison operators (=, <, >, <=, >=, IN).
• Index Requirements: For many queries (especially those with filters or sorts), Datastore requires indexes. Indexes are automatically created for simple queries (e.g., querying by key), but composite indexes (for multiple property filters or sorting) need to be defined in your index.yaml file. Datastore will automatically create suggested indexes for your to deploy in the index.yaml when a query can't be run using the built-in indexes.
• Limitations:
  * Equality filters (=) can only be used on a limited number of properties in a single query (generally just one without composite indexes).
  * Inequality filters (<, >, etc.) can generally only be applied to *one* property in a single query. To filter by multiple inequality values, consider pre-filtering your data, doing filtering on client code after receiving results.
  
• Keys-Only Queries: If you only need the keys of the entities, use a keys-only query for better performance and lower costs.
• Ancestor Queries: If your data is organized in a hierarchical structure using ancestor paths, you can perform queries that are scoped to a specific ancestor. These queries are strongly consistent.

6. Transactions in Datastore

Transactions allow you to perform multiple operations as a single atomic unit. This ensures that all operations either succeed or fail together, maintaining data consistency. Datastore supports ACID (Atomicity, Consistency, Isolation, Durability) transactions *within an entity group* (i.e., entities that share the same ancestor path).

Python Example: Performing a Transaction

 from google.cloud import datastore

 client = datastore.Client()

 def transfer_funds(from_account_key, to_account_key, amount):
 with client.transaction():
 from_account = client.get(from_account_key)
 to_account = client.get(to_account_key)

 if from_account is None or to_account is None:
 raise ValueError("One or both accounts not found")

 if from_account['balance'] < amount:
 raise ValueError("Insufficient funds")

 from_account['balance'] -= amount
 to_account['balance'] += amount

 client.put_multi([from_account, to_account])

 # Example Usage (assuming these account entities exist)
 account1_key = client.key('Account', 'account1')
 account2_key = client.key('Account', 'account2')

 try:
 transfer_funds(account1_key, account2_key, 100)
 print("Transaction successful!")
 except ValueError as e:
 print(f"Transaction failed: {e}")

 

Important Notes About Transactions:

• Datastore limits transactions to entity groups with the same ancestor. Consider the architecture if transaction spanning unrelated objects are often required, because if the use-cases fits the transaction across a set of entities, those entities will all need the same parent. Long, or highly contentious parent path can degrade write/read performance considerably.
• Transactions have time limits, keep them brief. Complex operations, heavy reads can timeout transactions, consider architectural refactor.
• Consider potential performance impact and optimize your data model to minimize contention within entity groups to optimize for maximum throughput of your application.

7. Best Practices for Using Datastore

To get the most out of Datastore, follow these best practices:

• Optimize Data Modeling: Carefully consider your data model and how you will query your data. Avoid overly complex data structures that lead to inefficient queries. Denormalize where necessary to optimize read performance.
• Use Keys Wisely: Understand the difference between ID-based and name-based keys. Using IDs assigned by Datastore is generally faster, especially when writing entities, so in many cases consider names over IDs if they provide valuable information.
• Manage Indexes: Regularly review and optimize your indexes. Remove unused indexes to reduce storage costs and improve write performance. Be mindful of the limits on composite indexes (maximum properties) to optimize costs and maintenance, or be cautious about increasing that default constraint.
• Avoid Hotspots: A hotspot is when one shard of data is experiencing heavier usage over others, causing slow response or read errors for requests across all data. This often occurs if writing data sequentially on increasing order which causes uneven data distribution. When storing a huge load of data with unique properties which tend to grow, randomize it, instead of storing in growing sequential numerical format.
• Batch Operations: When possible, use batch operations (e.g., put_multi(), delete_multi()) to improve performance.
• Monitor Performance: Regularly monitor the performance of your Datastore queries and transactions using Google Cloud Monitoring. Identify and address any performance bottlenecks.
• Plan for Growth: Design your data model with future scalability in mind. Consider how your data will be sharded and distributed as your application grows.
• Use a consistent property for timestamp and other numerical fields. Using both INTEGER and STRING for such kind of information can be disastrous in both short and long term. Make sure timestamp/datetime field are stored with correct data types.
  

8. Monitoring and Troubleshooting

Google Cloud Monitoring and Cloud Logging provide tools to monitor Datastore's performance and troubleshoot issues:

• Cloud Monitoring: Track key metrics like query latency, write operations, and storage usage. Set up alerts to be notified of potential problems.
• Cloud Logging: Analyze Datastore logs to identify errors, performance bottlenecks, and other issues.
• Datastore Admin (Cloud Console): The Cloud Console provides tools for browsing your data, managing indexes, and performing other administrative tasks.

Conclusion

Google Cloud Datastore (Firestore in Datastore mode) is a powerful and versatile NoSQL database solution that is well-suited for many types of applications. By understanding its key concepts, data modeling principles, querying capabilities, and best practices, you can build scalable and reliable applications that leverage the full potential of Datastore. Remember to choose appropriate indexing strategy for each type of query you are performing for both costs and performance improvement of queries performed within the Google Cloud environment.

Datastore Firestore NoSQL Database Data Modeling 

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